SoroushVahidi/scidocs huggingface dataset:
Self processes in interdependent relationships Partner affirmation and the Michelangelo phenomenon
(2005)

This essay reviews theory and research regarding the “Michelangelo phenomenon,” which describes the manner in which close partners shape one another’s dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior, exhibiting confidence in the self ’s capacity and enacting behaviors that elicit key features of the self ’s ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.
Receiver Design for Downlink Non-Orthogonal Multiple Access (NOMA)
(2015)

Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for further cellular enhancements toward 5G. Compared to orthogonal multiple access (OMA) such as orthogonal frequency-division multiple access (OFDMA), large performance gains were confirmed via system-level simulations. However, NOMA link-level simulations and the design of the receiver remain of great importance to validate NOMA performance gains. In this paper, we evaluate downlink NOMA link-level performance with multiple receiver designs and propose a novel NOMA transmitter and receiver design, where the signals of multi-users are jointly modulated at transmitter side and detected at receiver side. The predominant advantage of the proposed scheme is that at receiver side interference cancellation to the interference signal is not needed, thus low complexity is achieved. The performances of codeword-level SIC, symbol-level SIC and the proposed receiver are evaluated and compared with ideal SIC. Simulation results show that compared with ideal SIC, downlink NOMA link-level performance depends on actual receiver design and the difference in the power ratio split between the cell edge user and cell center user. In particular, it is shown that codeword-level SIC and the proposed receiver can both provide a good performance even when the power ratio difference between the cell center user and cell edge user is small and with real channel estimation.
Retrieval-Based Learning: Positive Effects of Retrieval Practice in Elementary School Children
(2016)

A wealth of research has demonstrated that practicing retrieval is a powerful way to enhance learning. However, nearly all prior research has examined retrieval practice with college students. Little is known about retrieval practice in children, and even less is known about possible individual differences in retrieval practice. In three experiments, 88 children (mean age 10 years) studied a list of words and either restudied the items or practiced retrieving them. They then took a final free recall test (Experiments 1 and 2) or recognition test (Experiment 3). In all experiments, children showed robust retrieval practice effects. Although a range of individual differences in reading comprehension and processing speed were observed among these children, the benefits of retrieval practice were independent of these factors. The results contribute to the growing body of research supporting the mnemonic benefits of retrieval practice and provide preliminary evidence that practicing retrieval may be an effective learning strategy for children with varying levels of reading comprehension and processing speed.
Human posture recognition using curved segments for image retrieval
(1999)

This paper presents a human posture recognition method from a single image. We rst segment an image into homogeneous regions and extract curve segments corresponding to human body parts. Each body part is considered as a 2D ribbon. From the smooth curve segments in skin regions, 2D ribbons are extracted and a human body model is constructed. We assign a predeened posture type to the image according to the constructed body model. For the user input query to retrieve images containing human of speciic posture, the system convert the query to a body model. The body model is compared to other body models saved in the local storage of target images and images of good matches are retrieved. When a face detection result is available for the given image, it is also used to increase the reliability of body model. For the query human posture, our system retrieves images of the corresponding posture. As another application, the proposed method provides an initial location of a human body to track in a video sequence.
A Study Investigating Typical Concepts and Guidelines for Ontology Building
(2015)

In semantic technologies, the shared common understanding of the structure of information among artifacts (people or software agents) can be realized by building an ontology. To do this, it is imperative for an ontology builder to answer several questions: a) what are the main components of an ontology? b) How an ontology look likes and how it works? c) Verify if it is required to consider reusing existing ontologies or not? c) What is the complexity of the ontology to be developed? d) What are the principles of ontology design and development? e) How to evaluate an ontology? This paper answers all the key questions above. The aim of this paper is to present a set of guiding principles to help ontology developers and also inexperienced users to answer such questions.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
(2013)

Optimal transport distances are a fundamental family of dis tances for probability measures and histograms of features. Despite their appeali ng theoretical properties, excellent performance in retrieval tasks and intuiti ve formulation, their computation involves the resolution of a linear program whose c ost an quickly become prohibitive whenever the size of the support of these me asur s or the histograms’ dimension exceeds a few hundred. We propose in this work a new family of optimal transport distances that look at transport probl ems from a maximumentropy perspective. We smooth the classic optimal transpo rt pr blem with an entropic regularization term, and show that the resulting o ptimum is also a distance which can be computed through Sinkhorn’s matrix scali ng algorithm at a speed that is several orders of magnitude faster than that of transport solvers. We also show that this regularized distance improves upon clas si optimal transport distances on the MNIST classification problem.
Supervised Word Mover's Distance
(2016)

Recently, a new document metric called the word mover’s distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric. The supervised training minimizes the stochastic leave-one-out nearest neighbor classification error on a perdocument level by updating an affine transformation of the underlying word embedding space and a word-imporance weight vector. As the gradient of the original WMD distance would result in an inefficient nested optimization problem, we provide an arbitrarily close approximation that results in a practical and efficient update rule. We evaluate S-WMD on eight real-world text classification tasks on which it consistently outperforms almost all of our 26 competitive baselines.
Siamese Recurrent Architectures for Learning Sentence Similarity
(2016)

We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide wordembedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.
Effective Feature Integration for Automated Short Answer Scoring
(2015)

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.
Automatic Segmentation of Hair in Images
(2015)

Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.
A clutched parallel elastic actuator concept: Towards energy efficient powered legs in prosthetics and robotics
(2012)

Parallel passive-elastic elements can reduce the energy consumption and torque requirements for motors in powered legged systems. However, the hardware design for such combined actuators is challenged by the need to engage and disengage the parallel elasticity depending on the gait phase. Although clutches in the drive train are often proposed, compact and low cost solutions of clutched parallel elastic actuators have so far not been established. Here we present the design and control of an initial prototype for a parallel elastic actuator. The actuator combines a DC motor with a parallel spring that is engaged and disengaged by a commercially available, compact and low-cost electric clutch. In experiments that mimic the torque and motion patterns of knee extensor muscles in human rebounding tasks we find that the parallel spring in the prototype reduces the energy consumption of the actuator by about 80% and the peak torque requirement for the DC motor by about 66%. In addition, we find that a simple trigger-based control can reliably engage and disengage the electric clutch during the motion, allowing the spring to support the motor in rebound, to remove stored energy from the system as necessary for stopping, and to virtually disappear at the actuator output level. On the other hand, the hardware experiments also reveal that our initial design limits the precision in the torque control, and we propose specific improvements to overcome these limitations.
Document Distance Estimation via Code Graph Embedding
(2017)

Accurately representing the distance between two documents (i.e. pieces of textual information extracted from various software artifacts) has far-reaching applications in many automated software engineering approaches, such as concept location, bug location and traceability link recovery. This is a challenging task, since documents containing different words may have similar semantic meanings. In this paper, we propose a novel document distance estimation approach. This approach captures latent semantic associations between documents through analyzing structural information in software source code: first, we embed code elements as points in a shared representation space according to structural dependencies between them; then, we represent documents as weighted point clouds of code elements in the representation space and reduce the distance between two documents to an earth mover's distance transportation problem. We define a document classification task in StackOverflow dataset to evaluate the effectiveness of our approach. The empirical evaluation results show that our approach outperforms several state-of-the-art approaches.
Traffic road line detection based on the vanishing point and contour information
(2011)

This paper describes an algorithm to detect the road lane boundary for a driver who changes lane as ‘access road’, ‘passing lane’, ‘approach ramp’, etc. The proposed algorithm starts with binarization and labeling for the image. The binary image is generated by adaptive threshold. The binary image is labeled recursively to blobs. From each blob, the algorithm analyses principal axes calculated by Eigen value and vector. Those are based on the covariance matrix of pixel distribution for the blobs. Then it shows intersection points by the combination of each long principal axis. Among intersection points, the highest density cluster is selected to a dominant vanishing point as the parallel road lines. In experiments of road line detection from the urban road image, the proposed method shows the effectiveness of lane boundary detection in the rate of more than 94.3%.
Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data.
(2014)

Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.
Absolute Orientation for Word Embedding Alignment
(2018)

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.
Cyberbullying and Primary-School Aged Children : The Psychological Literature and the Challenge for Sociology
(2015)

Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted OPEN ACCESS Societies 2015, 5 493 impacts, understanding and behaviours or engaged children’s independent voice. This review highlights current gaps in our knowledge about younger children’s experiences with this form of bullying, and the importance of employing cross-disciplinary and developmentally appropriate methodologies to inform future research.
Smooth contact-aware facial blendshapes transfer
(2013)

In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.
Educational Software Applied in Teaching Electrocardiogram: A Systematic Review
(2018)

Background
The electrocardiogram (ECG) is the most used diagnostic tool in medicine; in this sense, it is essential that medical undergraduates learn how to interpret it correctly while they are still on training. Naturally, they go through classic learning (e.g., lectures and speeches). However, they are not often efficiently trained in analyzing ECG results. In this regard, methodologies such as other educational support tools in medical practice, such as educational software, should be considered a valuable approach for medical training purposes.


Methods
We performed a literature review in six electronic databases, considering studies published before April 2017. The resulting set comprises 2,467 studies. From this collection, 12 studies have been selected, initially, whereby we carried out a snowballing process to identify other relevant studies through the reference lists of these studies, resulting in five relevant studies, making up a total of 17 articles that passed all stages and criteria.


Results
The results show that 52.9% of software types were tutorial and 58.8% were designed to be run locally on a computer. The subjects were discussed together with a greater focus on the teaching of electrophysiology and/or cardiac physiology, identifying patterns of ECG and/or arrhythmias.


Conclusions
We found positive results with the introduction of educational software for ECG teaching. However, there is a clear need for using higher quality research methodologies and the inclusion of appropriate controls, in order to obtain more precise conclusions about how beneficial the inclusion of such tools can be for the practices of ECG interpretation.
Optimal dimensioning of active cell balancing architectures
(2014)

This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.
Algorithms for Unevenly Spaced Time Series : Moving Averages and Other Rolling Operators
(2015)

This paper describes algorithms for efficiently calculating certain rolling time series operators for unevenly spaced data. In particular, we show how to calculate simple moving averages (SMAs), exponential moving averages (EMAs), and related operators in linear time with respect to the number of observations in a time series. A web appendix provides an implementation of these algorithms in the programming language C and a package (forthcoming) for the statistical software R.